Identification method of driver response behaviors under group induction information

Abstract

The invention discloses an identification method of driver response behaviors under group induction information. The identification method comprises the following steps of collecting and processing traffic information, building a driver response behavior model and updating mold coefficients; obtaining cross section traffic flow rate information, traffic state information, stroke time information, accident information and traffic information issued by variable message signs by utilizing traffic information detecting equipment, and obtaining corresponding split flow proportion and corresponding information obedience rate; converting the corresponding information obedience rate into the information obedience rate by using the reference traffic state; building the driver response behavior model, and obtaining the steady-state model coefficient through regression; and updating the model coefficient by using the self-adaption Kalman filter technology according to the steady-state model coefficient and the split flow point upstream and downstream detection traffic flow rate, and realizing the automatic identification on the driver response behaviors. The identification method has the advantages that multi-source detection traffic data is fully utilized, the traffic behavior collection reliability is improved, the cost is saved, and the foundation is laid for the implementation of active intelligent traffic induction.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, in particular to an identification method for driver response behavior under group-induced information. Background technique [0002] In order to deal with the increasingly serious problem of urban traffic congestion, many large cities at home and abroad have launched their own research and development, promotion and application of traffic guidance systems based on variable information boards. Now they have a certain scale and achieved certain expected results. . However, with the continuous growth of traffic demand, the passive induction method based on "real-time detection of traffic status" restricts its induction effect and level. Dynamic deduction and prediction of traffic operation situation" active intelligent induction improvement. Accurately grasping the driver's response behavior under group guidance information is the premise of implementing active intelligent traf...

AI Technical Summary

Problems solved by technology

[0003] At present, intention surveys or explicit preference surveys based on on-site surveys, emails, telephone inquiries, and travel simulators are often used to evaluate drivers’ response behaviors and traffic diversion effects under the influence of traffic information, although the responses of respondents can vary to a certain extent. To some extent, it reflects its behavior, but questionnaires and simulations based on hypothetical scenarios may be unreliable, which is reflected in the fact that the same traffic information has different effects on the variable information boards in different locations. Even in the same location, the variable information boards The effect of different driver groups, traffic conditions and road network attributes also changes, so the evaluation conclusions of the diversion effect of the existing variable information boards are not consistent

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